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Social Web Mining 2011 - Barcelona

International Workshop on Social Web Mining, Barcelona 2011

There is an increasing interest in social web mining, as we can see from the ACM workshop on Social Web Search and Analysis. It is not until recently that great progresses have been made in mining social network for various applications, e.g., making personalized recommendations. This workshop focuses on the study of diverse aspects of social networks with their applications in domains including mobile recommendations, service providers, electronic commerce, etc.

Social networks have actually played an important role in different domains for about a decade, particularly in recommender systems. In general, traditional collaborative filtering approaches can be considered as making personalized recommendations based on implicit social interaction, where social connections are defined by some similarity metrics on common rated items, e.g., movies for the Netflix Prize.
With the recent development of Web 2.0, there emerges a number of globally deployed applications for explicit social interactions, such as Facebook, Flickr, LinkedIn, Twitter, etc. These applications have been exploited by academic institutions and industries to build modern recommender systems based on social networks, e.g., Microsoft's Project Emporia that recommends tweets to user based on their behaviors.

In recent years, rapid progress has been made in the study of social networks for diverse applications. For instance, researchers have proposed various tensor factorization techniques to analyze user-item-tag data in Flickr for group recommendations. Also, researchers study Facebook to infer users' preferences.

However, there exist many challenges in mining social web and its application in recommender systems. Some are:

What is the topology of social networks for some specific application like LinkedIn?

How could one build optimal models for social networks such as Facebook?

How can one handle the privacy issue caused by utilizing social interactions for making recommendation?

How could one model a user's preferences based on his/her social interactions?